The present invention relates to an improvement in an adaptive differential pulse code modulation (ADPCM) system for the frequency band compression of speech or like signals.
Various techniques have been developed to transmit or store speech signals in compressed form utilizing the redundancy of a speech signal. A differential pulse code modulation (DPCM) system achieves such band compression by making use of the characteristics of the speech signal. This is based on the fact that speech signal samples have a close correlation with each other. More specifically, the prediction of the amplitude of each sample of the speech signal at a given point in time can be made on the basis of the past speech signal sample. The simplest DPCM method uses as a predicted value a sample immediately preceding the present sample or the product of that preceding sample and a value slightly smaller than 1, and transmits or stores only the prediction error signal resulting from the subtraction of the predicted value from the true amplitude. The original signal can be reproduced by similarly producing a predicted value and adding to it the prediction error signal in the reproduction unit. The compression of speech signals by this DPCM system improves the signal to noise (S/N) ratio by about 6 dB (decibel) over a PCM system using the same number of bits. For the same S/N ratio, a DPCM system can save about one bit per sample as compared to a PCM system.
Compression efficiency can be further raised by using a plurality of past samples for the prediction instead of only one, thus enabling more accurate prediction. Prediction accuracy can also be improved by adapting the prediction method to the characteristics of the particular signal to be predicted. To be more specific, a predicted value X.sub.j of a speech signal having an amplitude X.sub.j at a time point j is represented by: EQU X.sub.j =A.sub.1 X.sub.j-1 +A.sub.2 X.sub.j-2 + . . . +A.sub.n X.sub.j-n
where A.sub.1, A.sub.2, . . . A.sub.n are the coefficients of a prediction filter. Adaptive prediction means the optimum selection of these prediction filter coefficients for the speech signal to be predicted.
The method for obtaining the prediction coefficients can be broadly classified into two categories: one is to analyze a speech signal for the optimum prediction coefficients, and the other is to observe the prediction error signal and adaptively correct the prediction coefficients so as to reduce the prediction error signal. While the former requires transmission of both the quantized signal and the prediction coefficients obtained from the speech signal analysis, the latter needs no transmission of the prediction coefficients. Accordingly, the latter results in a simpler circuit construction.
For details of an ADPCM system using this method, reference is made to a paper by David L. Cohn et al, entitled "The Residual Encoder--An Improved ADPCM System for Speech Digitization", IEEE Transactions on Communications, Vol. COM-23, No. 9, September issue, 1975, pp. 935-941 (hereinafter referred to as Reference 1). However, this ADPCM system, which is vulnerable to transmission errors, needs additional hardware to eliminate said errors at the sacrifice of the S/N ratio, resulting in a larger and more expensive system.